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COVID-19 sentiment analysis on Facebook comments

Ibrahim, Sajidah and Ab. Rahim, Nor Zairah and Ibnu Fatihan, Fajar and Abu Bakar, Nur Azaliah (2021) COVID-19 sentiment analysis on Facebook comments. International Journal of Modern Trends in Social Sciences (IJMTSS), 4 (17). pp. 1-8. ISSN 2600-8777


Official URL: http://dx.doi.org/10.35631/IJMTSS.417001


Malaysia recorded its first COVID-19 case on 9th March 2020 and recorded a total of 59,817 by end of November 2020. Buzz in social media over COVID-19 and measures by the Government to curb infection spread among citizens. The study aims to understand Malaysian public awareness and perception of COVID-19 related issues on Facebook during the 1st and 2nd week of Movement Control Order (MCO). Data mining was conducted on DG Tan Sri Noor Hisham Abdullah’s official Facebook account user comments and a total of 77,351 comments was collected between 18 March and 14 April 2020. The analyses included data pre-processing and sentiment analysis to identify and explore sentiments in discussion topics within the first two weeks of lockdown. The results yield majority of comments are in the Malay language and mix languages of English and Malay as a secondary type. Secondly, sentiment analysis showed that people have a positive reaction towards the frontliners and all efforts by the Ministry of Health towards fighting the pandemic. Many positive remarks are given in form of prayers, which is in line with the Islamic teaching of positive thinking and optimism, especially during crises. In conclusion, sentiment analysis is effective in producing useful insights about trends of COVID-19 discussion on social media, collecting public perception and feedback of COVID-19 efforts by the Government, and gives a different viewing angle of the current situation on the ground. These findings can be useful for health officials or the Government in developing communication mitigation plans or conduct extensive studies on pertaining issues within areas of concern.

Item Type:Article
Uncontrolled Keywords:sentiment analysis, COVID-19, machine learning, social media
Subjects:H Social Sciences > H Social Sciences (General)
T Technology > T Technology (General) > T58.5-58.64 Information technology
Divisions:Razak School of Engineering and Advanced Technology
ID Code:97923
Deposited By: Yanti Mohd Shah
Deposited On:07 Nov 2022 10:42
Last Modified:07 Nov 2022 10:42

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